





'''simple docstring'''






import unittest

import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModel

from diffusers import DDIMScheduler, LDMPipeline, UNetaDModel, VQModel
from diffusers.utils.testing_utils import enable_full_determinism, require_torch, slow, torch_device


enable_full_determinism()
class 		lowerCamelCase	(   unittest.TestCase							):



	@property
	def 	_lowerCamelCase     ( self						):
			torch.manual_seed(0						)
			lowerCAmelCase  :     Dict       						= UNetaDModel(
			    block_out_channels=(32, 64)		,   layers_per_block=2		,   sample_size=32		,   in_channels=3		,   out_channels=3		,   down_block_types=("DownBlock2D", "AttnDownBlock2D")		,   up_block_types=("AttnUpBlock2D", "UpBlock2D")		,   )
			return model



	@property
	def 	_lowerCamelCase     ( self						):
			torch.manual_seed(0						)
			lowerCAmelCase  :     Dict       						= VQModel(
			    block_out_channels=[32, 64]		,   in_channels=3		,   out_channels=3		,   down_block_types=["DownEncoderBlock2D", "DownEncoderBlock2D"]		,   up_block_types=["UpDecoderBlock2D", "UpDecoderBlock2D"]		,   latent_channels=3		,   )
			return model



	@property
	def 	_lowerCamelCase     ( self						):
			torch.manual_seed(0						)
			lowerCAmelCase  :     List[Any]       						= CLIPTextConfig(
			    bos_token_id=0		,   eos_token_id=2		,   hidden_size=32		,   intermediate_size=37		,   layer_norm_eps=1e-0_5		,   num_attention_heads=4		,   num_hidden_layers=5		,   pad_token_id=1		,   vocab_size=1_000		,   )
			return CLIPTextModel(a_						)





	def 	_lowerCamelCase     ( self						):
			lowerCAmelCase  :     Union[str, Any]       						= self.dummy_uncond_unet
			lowerCAmelCase  :     List[Any]       						= DDIMScheduler()
			lowerCAmelCase  :     Tuple       						= self.dummy_vq_model

			lowerCAmelCase  :     Optional[int]       						= LDMPipeline(unet=a_		,   vqvae=a_		,   scheduler=a_						)
			ldm.to(a_						)
			ldm.set_progress_bar_config(disable=a_						)

			lowerCAmelCase  :     Optional[Any]       						= torch.manual_seed(0						)
			lowerCAmelCase  :     Tuple       						= ldm(generator=a_		,   num_inference_steps=2		,   output_type="numpy"						).images

			lowerCAmelCase  :     Dict       						= torch.manual_seed(0						)
			lowerCAmelCase  :     Any       						= ldm(generator=a_		,   num_inference_steps=2		,   output_type="numpy"		,   return_dict=a_						)[0]

			lowerCAmelCase  :     Any       						= image[0, -3:, -3:, -1]
			lowerCAmelCase  :     str       						= image_from_tuple[0, -3:, -3:, -1]

			assert image.shape == (1, 64, 64, 3)
			lowerCAmelCase  :     Union[str, Any]       						= np.array([0.8512, 0.818, 0.6411, 0.6808, 0.4465, 0.5618, 0.46, 0.6231, 0.5172]						)
			lowerCAmelCase  :     str       						= 1e-2 if torch_device != "mps" else 3e-2

			assert np.abs(image_slice.flatten() - expected_slice						).max() < tolerance
			assert np.abs(image_from_tuple_slice.flatten() - expected_slice						).max() < tolerance






@slow
@require_torch
class 		lowerCamelCase	(   unittest.TestCase							):



	def 	_lowerCamelCase     ( self						):
			lowerCAmelCase  :     List[Any]       						= LDMPipeline.from_pretrained("CompVis/ldm-celebahq-256"						)
			ldm.to(a_						)
			ldm.set_progress_bar_config(disable=a_						)

			lowerCAmelCase  :     List[Any]       						= torch.manual_seed(0						)
			lowerCAmelCase  :     Any       						= ldm(generator=a_		,   num_inference_steps=5		,   output_type="numpy"						).images

			lowerCAmelCase  :     str       						= image[0, -3:, -3:, -1]

			assert image.shape == (1, 256, 256, 3)
			lowerCAmelCase  :     Tuple       						= np.array([0.4399, 0.44975, 0.46825, 0.474, 0.4359, 0.4581, 0.45095, 0.4341, 0.4447]						)
			lowerCAmelCase  :     int       						= 1e-2 if torch_device != "mps" else 3e-2

			assert np.abs(image_slice.flatten() - expected_slice						).max() < tolerance










'''simple docstring'''






import fire

from transformers import AutoConfig, AutoModelForSeqaSeqLM, AutoTokenizer






def   __A    (				a_    :	str   ,a_    :	str   ,**a_    :	int						):
		lowerCAmelCase  :     str       						= AutoConfig.from_pretrained(a_   ,**a_						)
		lowerCAmelCase  :     Any       						= AutoModelForSeqaSeqLM.from_config(a_						)
		model.save_pretrained(a_						)
		AutoTokenizer.from_pretrained(a_						).save_pretrained(a_						)
		return model


if __name__ == "__main__":
					fire.Fire(save_randomly_initialized_version)



